Update app.py

app.py

@@ -1,64 +1,39 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("Neo111x/falcon3-decompiler-3b-v1.5")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.0, maximum=4.0, value=0.0, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load the model and tokenizer
+model_path = 'LLM4Binary/llm4decompile-1.3b-v1.5'  # V1.5 Model
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.bfloat16).cuda()
+
+# Define the inference function
+def generate_response(input_text, temperature, top_k, top_p):
+    inputs = tokenizer(input_text, return_tensors="pt")
+    outputs = model.generate(
+        **inputs,
+        max_length=512,  # Adjust this if needed
+        do_sample=True,
+        top_k=int(top_k),
+        top_p=float(top_p),
+        temperature=float(temperature)
+    )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Create a Gradio interface with sliders
+interface = gr.Interface(
+    fn=generate_response,
+    inputs=[
+        gr.Textbox(lines=5, placeholder="Enter your input text here...", label="Input Text"),
+        gr.Slider(0.1, 2.0, value=0.0, step=0.1, label="Temperature"),
+        gr.Slider(1, 100, value=10, step=1, label="Top-k"),
+        gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-p")
     ],
+    outputs=gr.Textbox(label="Generated Response"),
+    title="LLM4Binary Interactive Demo",
+    description="Adjust the sliders for temperature, top-k, and top-p to customize the model's response."
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+# Launch the Gradio app
+interface.launch()